{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TimeGAN Tutorial\n",
    "\n",
    "## Time-series Generative Adversarial Networks\n",
    "\n",
    "- Paper: Jinsung Yoon, Daniel Jarrett, Mihaela van der Schaar, \"Time-series Generative Adversarial Networks,\" Neural Information Processing Systems (NeurIPS), 2019.\n",
    "\n",
    "- Paper link: https://papers.nips.cc/paper/8789-time-series-generative-adversarial-networks\n",
    "\n",
    "- Last updated Date: April 24th 2020\n",
    "\n",
    "- Code author: Jinsung Yoon (jsyoon0823@gmail.com)\n",
    "\n",
    "This notebook describes the user-guide of a time-series synthetic data generation application using timeGAN framework. We use Stock, Energy, and Sine dataset as examples.\n",
    "\n",
    "### Prerequisite\n",
    "Clone https://github.com/jsyoon0823/timeGAN.git to the current directory."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Necessary packages and functions call\n",
    "\n",
    "- timegan: Synthetic time-series data generation module\n",
    "- data_loading: 2 real datasets and 1 synthetic datasets loading and preprocessing\n",
    "- metrics: \n",
    "    - discriminative_metrics: classify real data from synthetic data\n",
    "    - predictive_metrics: train on synthetic, test on real\n",
    "    - visualization: PCA and tSNE analyses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Necessary packages\n",
    "from __future__ import absolute_import\n",
    "from __future__ import division\n",
    "from __future__ import print_function\n",
    "\n",
    "import numpy as np\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# 1. TimeGAN model\n",
    "from timegan import timegan\n",
    "# 2. Data loading\n",
    "from data_loading import real_data_loading, sine_data_generation\n",
    "# 3. Metrics\n",
    "from metrics.discriminative_metrics import discriminative_score_metrics\n",
    "from metrics.predictive_metrics import predictive_score_metrics\n",
    "from metrics.visualization_metrics import visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Loading\n",
    "\n",
    "Load original dataset and preprocess the loaded data.\n",
    "\n",
    "- data_name: stock, energy, or sine\n",
    "- seq_len: sequence length of the time-series data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stock dataset is ready.\n"
     ]
    }
   ],
   "source": [
    "## Data loading\n",
    "data_name = 'stock'\n",
    "seq_len = 24\n",
    "\n",
    "if data_name in ['stock', 'energy']:\n",
    "  ori_data = real_data_loading(data_name, seq_len)\n",
    "elif data_name == 'sine':\n",
    "  # Set number of samples and its dimensions\n",
    "  no, dim = 10000, 5\n",
    "  ori_data = sine_data_generation(no, seq_len, dim)\n",
    "    \n",
    "print(data_name + ' dataset is ready.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set network parameters\n",
    "\n",
    "TimeGAN network parameters should be optimized for different datasets.\n",
    "\n",
    "- module: gru, lstm, or lstmLN\n",
    "- hidden_dim: hidden dimensions\n",
    "- num_layer: number of layers\n",
    "- iteration: number of training iterations\n",
    "- batch_size: the number of samples in each batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Newtork parameters\n",
    "parameters = dict()\n",
    "\n",
    "parameters['module'] = 'gru' \n",
    "parameters['hidden_dim'] = 24\n",
    "parameters['num_layer'] = 3\n",
    "parameters['iterations'] = 10000\n",
    "parameters['batch_size'] = 128"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run TimeGAN for synthetic time-series data generation\n",
    "\n",
    "TimeGAN uses the original data and network parameters to return the generated synthetic data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/vdslab/Documents/Jinsung/2019_Research/NIPS/TGAN/My_GitHub/timegan.py:38: The name tf.reset_default_graph is deprecated. Please use tf.compat.v1.reset_default_graph instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/vdslab/Documents/Jinsung/2019_Research/NIPS/TGAN/My_GitHub/timegan.py:80: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/vdslab/Documents/Jinsung/2019_Research/NIPS/TGAN/My_GitHub/timegan.py:94: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/vdslab/Documents/Jinsung/2019_Research/NIPS/TGAN/My_GitHub/timegan.py:94: The name tf.AUTO_REUSE is deprecated. Please use tf.compat.v1.AUTO_REUSE instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/vdslab/Documents/Jinsung/2019_Research/NIPS/TGAN/My_GitHub/utils.py:95: GRUCell.__init__ (from tensorflow.python.ops.rnn_cell_impl) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "This class is equivalent as tf.keras.layers.GRUCell, and will be replaced by that in Tensorflow 2.0.\n",
      "WARNING:tensorflow:From /home/vdslab/Documents/Jinsung/2019_Research/NIPS/TGAN/My_GitHub/timegan.py:95: MultiRNNCell.__init__ (from tensorflow.python.ops.rnn_cell_impl) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "This class is equivalent as tf.keras.layers.StackedRNNCells, and will be replaced by that in Tensorflow 2.0.\n",
      "WARNING:tensorflow:From /home/vdslab/Documents/Jinsung/2019_Research/NIPS/TGAN/My_GitHub/timegan.py:96: dynamic_rnn (from tensorflow.python.ops.rnn) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `keras.layers.RNN(cell)`, which is equivalent to this API\n",
      "WARNING:tensorflow:From /home/vdslab/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/ops/rnn_cell_impl.py:559: Layer.add_variable (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `layer.add_weight` method instead.\n",
      "WARNING:tensorflow:From /home/vdslab/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/ops/rnn_cell_impl.py:565: calling Constant.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
      "WARNING:tensorflow:From /home/vdslab/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/ops/rnn_cell_impl.py:575: calling Zeros.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
      "WARNING:tensorflow:From /home/vdslab/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/ops/rnn.py:244: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
      "WARNING:tensorflow:\n",
      "The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
      "For more information, please see:\n",
      "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
      "  * https://github.com/tensorflow/addons\n",
      "  * https://github.com/tensorflow/io (for I/O related ops)\n",
      "If you depend on functionality not listed there, please file an issue.\n",
      "\n",
      "WARNING:tensorflow:From /home/vdslab/anaconda3/lib/python3.7/site-packages/tensorflow_core/contrib/layers/python/layers/layers.py:1866: Layer.apply (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `layer.__call__` method instead.\n",
      "WARNING:tensorflow:From /home/vdslab/Documents/Jinsung/2019_Research/NIPS/TGAN/My_GitHub/timegan.py:182: The name tf.trainable_variables is deprecated. Please use tf.compat.v1.trainable_variables instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/vdslab/Documents/Jinsung/2019_Research/NIPS/TGAN/My_GitHub/timegan.py:189: The name tf.losses.sigmoid_cross_entropy is deprecated. Please use tf.compat.v1.losses.sigmoid_cross_entropy instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/vdslab/Documents/Jinsung/2019_Research/NIPS/TGAN/My_GitHub/timegan.py:200: The name tf.losses.mean_squared_error is deprecated. Please use tf.compat.v1.losses.mean_squared_error instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/vdslab/Documents/Jinsung/2019_Research/NIPS/TGAN/My_GitHub/timegan.py:217: The name tf.train.AdamOptimizer is deprecated. Please use tf.compat.v1.train.AdamOptimizer instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/vdslab/Documents/Jinsung/2019_Research/NIPS/TGAN/My_GitHub/timegan.py:224: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
      "\n",
      "WARNING:tensorflow:From /home/vdslab/Documents/Jinsung/2019_Research/NIPS/TGAN/My_GitHub/timegan.py:225: The name tf.global_variables_initializer is deprecated. Please use tf.compat.v1.global_variables_initializer instead.\n",
      "\n",
      "Start Embedding Network Training\n",
      "step: 0/10000, e_loss: 0.3479\n",
      "step: 1000/10000, e_loss: 0.0201\n",
      "step: 2000/10000, e_loss: 0.0151\n",
      "step: 3000/10000, e_loss: 0.0131\n",
      "step: 4000/10000, e_loss: 0.0106\n",
      "step: 5000/10000, e_loss: 0.0086\n",
      "step: 6000/10000, e_loss: 0.006\n",
      "step: 7000/10000, e_loss: 0.005\n",
      "step: 8000/10000, e_loss: 0.004\n",
      "step: 9000/10000, e_loss: 0.0034\n",
      "Finish Embedding Network Training\n",
      "Start Training with Supervised Loss Only\n",
      "step: 0/10000, s_loss: 0.273\n",
      "step: 1000/10000, s_loss: 0.0191\n",
      "step: 2000/10000, s_loss: 0.009\n",
      "step: 3000/10000, s_loss: 0.0071\n",
      "step: 4000/10000, s_loss: 0.0054\n",
      "step: 5000/10000, s_loss: 0.0054\n",
      "step: 6000/10000, s_loss: 0.0042\n",
      "step: 7000/10000, s_loss: 0.0041\n",
      "step: 8000/10000, s_loss: 0.003\n",
      "step: 9000/10000, s_loss: 0.0028\n",
      "Finish Training with Supervised Loss Only\n",
      "Start Joint Training\n",
      "step: 0/10000, d_loss: 2.1664, g_loss_u: 0.6395, g_loss_s: 0.0217, g_loss_v: 0.3494, e_loss_t0: 0.0593\n",
      "step: 1000/10000, d_loss: 1.5046, g_loss_u: 1.3003, g_loss_s: 0.0064, g_loss_v: 0.0278, e_loss_t0: 0.0051\n",
      "step: 2000/10000, d_loss: 1.7552, g_loss_u: 1.164, g_loss_s: 0.019, g_loss_v: 0.0561, e_loss_t0: 0.0039\n",
      "step: 3000/10000, d_loss: 1.7624, g_loss_u: 1.1405, g_loss_s: 0.0067, g_loss_v: 0.0345, e_loss_t0: 0.0024\n",
      "step: 4000/10000, d_loss: 1.6768, g_loss_u: 1.1746, g_loss_s: 0.0051, g_loss_v: 0.0381, e_loss_t0: 0.0028\n",
      "step: 5000/10000, d_loss: 1.8292, g_loss_u: 1.0593, g_loss_s: 0.0071, g_loss_v: 0.0525, e_loss_t0: 0.0029\n",
      "step: 6000/10000, d_loss: 1.8503, g_loss_u: 1.0231, g_loss_s: 0.0049, g_loss_v: 0.0376, e_loss_t0: 0.0033\n",
      "step: 7000/10000, d_loss: 1.7555, g_loss_u: 1.1473, g_loss_s: 0.0071, g_loss_v: 0.0211, e_loss_t0: 0.003\n",
      "step: 8000/10000, d_loss: 1.6186, g_loss_u: 1.2681, g_loss_s: 0.0063, g_loss_v: 0.059, e_loss_t0: 0.0026\n",
      "step: 9000/10000, d_loss: 1.7353, g_loss_u: 1.3472, g_loss_s: 0.0098, g_loss_v: 0.0749, e_loss_t0: 0.0035\n",
      "Finish Joint Training\n",
      "Finish Synthetic Data Generation\n"
     ]
    }
   ],
   "source": [
    "# Run TimeGAN\n",
    "generated_data = timegan(ori_data, parameters)   \n",
    "print('Finish Synthetic Data Generation')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate the generated data\n",
    "\n",
    "### 1. Discriminative score\n",
    "\n",
    "To evaluate the classification accuracy between original and synthetic data using post-hoc RNN network. The output is |classification accuracy - 0.5|.\n",
    "\n",
    "- metric_iteration: the number of iterations for metric computation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/vdslab/Documents/Jinsung/2019_Research/NIPS/TGAN/My_GitHub/metrics/discriminative_metrics.py:81: all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.\n",
      "Instructions for updating:\n",
      "Please use tf.global_variables instead.\n",
      "Discriminative score: 0.1759\n"
     ]
    }
   ],
   "source": [
    "metric_iteration = 5\n",
    "\n",
    "discriminative_score = list()\n",
    "for _ in range(metric_iteration):\n",
    "  temp_disc = discriminative_score_metrics(ori_data, generated_data)\n",
    "  discriminative_score.append(temp_disc)\n",
    "\n",
    "print('Discriminative score: ' + str(np.round(np.mean(discriminative_score), 4)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate the generated data\n",
    "\n",
    "### 2. Predictive score\n",
    "\n",
    "To evaluate the prediction performance on train on synthetic, test on real setting. More specifically, we use Post-hoc RNN architecture to predict one-step ahead and report the performance in terms of MAE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/vdslab/Documents/Jinsung/2019_Research/NIPS/TGAN/My_GitHub/metrics/predictive_metrics.py:81: The name tf.losses.absolute_difference is deprecated. Please use tf.compat.v1.losses.absolute_difference instead.\n",
      "\n",
      "Predictive score: 0.0412\n"
     ]
    }
   ],
   "source": [
    "predictive_score = list()\n",
    "for tt in range(metric_iteration):\n",
    "  temp_pred = predictive_score_metrics(ori_data, generated_data)\n",
    "  predictive_score.append(temp_pred)   \n",
    "    \n",
    "print('Predictive score: ' + str(np.round(np.mean(predictive_score), 4)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate the generated data\n",
    "\n",
    "### 3. Visualization\n",
    "\n",
    "We visualize the original and synthetic data distributions using PCA and tSNE analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[t-SNE] Computing 121 nearest neighbors...\n",
      "[t-SNE] Indexed 2000 samples in 0.001s...\n",
      "[t-SNE] Computed neighbors for 2000 samples in 0.075s...\n",
      "[t-SNE] Computed conditional probabilities for sample 1000 / 2000\n",
      "[t-SNE] Computed conditional probabilities for sample 2000 / 2000\n",
      "[t-SNE] Mean sigma: 0.031771\n",
      "[t-SNE] KL divergence after 250 iterations with early exaggeration: 54.970322\n",
      "[t-SNE] KL divergence after 300 iterations: 0.729995\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualization(ori_data, generated_data, 'pca')\n",
    "visualization(ori_data, generated_data, 'tsne')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
